Abstract

It has been established in this study that the Rapid Visco Analyser (RVA) can describe maize hardness, irrespective of the RVA profile, when used in association with appropriate multivariate data analysis techniques. Therefore, the RVA can complement or replace current and/or conventional methods as a hardness descriptor. Hardness modelling based on RVA viscograms was carried out using seven conventional hardness methods (hectoliter mass (HLM), hundred kernel mass (HKM), particle size index (PSI), percentage vitreous endosperm (%VE), protein content, percentage chop (%chop) and near infrared (NIR) spectroscopy) as references and three different RVA profiles (hard, soft and standard) as predictors. An approach using locally weighted partial least squares (LW-PLS) was followed to build the regression models. The resulted prediction errors (root mean square error of cross-validation (RMSECV) and root mean square error of prediction (RMSEP)) for the quantification of hardness values were always lower or in the same order of the laboratory error of the reference method. (C) 2014 Elsevier Ltd. All rights reserved.

Keywords

White maize; Maize hardness; Milling quality; Conventional hardness methods; Rapid Visco Analyser (RVA); Chemometrics; Locally weighted partial least squares (LWPLS) regression

Published in

Food Chemistry
2015, Volume: 173, pages: 1220-1227
Publisher: ELSEVIER SCI LTD

SLU Authors

• Geladi, Paul

  • Department of Forest Biomaterials and Technology, Swedish University of Agricultural Sciences
    
  • Stellenbosch University

UKÄ Subject classification

Other Chemistry Topics


Publication identifier

DOI: https://doi.org/10.1016/j.foodchem.2014.10.149

Permanent link to this page (URI)

https://res.slu.se/id/publ/69151